A cellular mobile communication network in which each serving base station (BS) is equipped with an M-antenna array, is referred to as a large-scale multiuser multiple-input multiple-output (MIMO) system or a massive MIMO system if M>>1 and M>>K, where K is the number of active user antennas within its serving area. A massive MIMO system has the potential of achieving transmission rate much higher than those offered by current cellular systems with enhanced reliability and drastically improved power efficiency. It takes advantage of the so-called channel-hardening effect that implies that the channel vectors seen by different users tend to be mutually orthogonal and frequency-independent. As a result, linear receiver is almost optimal in the uplink and simple multiuser pre-coders are sufficient to guarantee satisfactory downlink performance.
To achieve such performance, channel state information (CSI) is needed for a variety of link adaptation applications such as precoder, modulation and coding scheme (SCM) selection. CSI in general includes large-scale fading coefficients (LSFCs) and small-scale fading coefficients (SSFCs). LFCSs summarize the pathloss and shadowing effects, which are proportional to the average received-signal-strength (RSS) and are useful in power control, location estimation, handover protocol, and other application. SSFCs, on the other hand, characterize the rapid amplitude fluctuations of the received signal. While all existing MIMO channel estimation focus on the estimation of the SSFCs and either ignore or assume perfect known LFCSs, it is desirable to know SSFCs and LSFCs separately. This is because LSFCs can not only be used for the aforementioned applications, but also be used for the accurate estimation of SSFCs.
LSFCs are long-term statistics whose estimation is often more time-consuming than SSFCs estimation. Conventional MIMO CSI estimation usually assume perfect LSFC information and deal solely with SSFCs. For co-located MIMO systems, it is reasonable to assume that the corresponding LSFCs remain constant across all spatial sub-channels and the SSFC estimation can sometime be obtained without the LSFC information. Such assumption is no longer valid in a multiuser MIMO system, where the user-BS distances spread over a large range and the SSFCs cannot be derived without the knowledge of LSFCs.
In the past, the estimation of LSFC has been largely neglected, assuming somehow perfectly known prior to SSFC estimation. When one needs to obtain a joint LSFC and SSFC estimate, the minimum mean square error (MMSE) or least squares (LS) criterion is not directly applicable. The expectation-maximization (EM) approach is a feasible alternate but it requires high computational complexity and convergence is not guaranteed. A solution for efficiently estimating LSFCs with no aid of SSFCs is sought in a massive multiuser MIMO system.